The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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scrutiny provoked defiant responses (Sherman, 1993) resulting in failure? Or do failure rates increase for low-risk probationers under more intensive supervision because the increase in the overall probability <strong>of</strong> detection is compared to a lower baseline? This paper considers whether an experiment designed to test the premise that lowrisk <strong>of</strong>fenders can safely receive less intensive supervision than the ‘standard’ model <strong>of</strong> probation is sensitive to heterogeneity in the type <strong>of</strong> low-risk <strong>of</strong>fender receiving treatment. As the willingness to use probation for <strong>of</strong>fenders eligible for prison persists, more resources will be needed to serve their needs and adequately protect the public. If the low-risk, low-need <strong>of</strong>fenders can be supervised more efficiently, probation <strong>of</strong>ficers will be freed up to focus on more serious cases. This sensitivity analysis mounts a comprehensive ‘attack’ on the low-intensity supervision model to ensure there is no way in which a policy reducing supervision for a section <strong>of</strong> the criminal population could increase the threat <strong>of</strong> harm to society. More broadly, we investigate the nature <strong>of</strong> lowrisk <strong>of</strong>fenders and their propensity to re<strong>of</strong>fend. <strong>The</strong> criminological literature has largely focused on the risk factors and characteristics <strong>of</strong> higher-level <strong>of</strong>fenders, but these serious cases make up a much smaller proportion <strong>of</strong> the criminal population. This paper examines the characteristics <strong>of</strong> low-level <strong>of</strong>fenders and their relationship with treatment effects. If the observed heterogeneity in the sample does not impact outcomes on lowintensity supervision, it seems reasonable to conclude that low-intensity supervision is a viable policy option compared to ‘treatment as usual.’ 55
What Works in Probation Supervision? Despite being one <strong>of</strong> the most widely-used criminal sanctions in the U.S.A., with one in forty-five adults on probation or parole (Glaze & Bonczar, 2009), much <strong>of</strong> the research on probation has failed to shed light on the characteristics <strong>of</strong> effective supervision practice. Correctional research has mainly focused on programming and treatments provided in addition to criminal justice sanctions like probation orders. In many probation agencies, standard practice is driven by resource constraints more than evidence-based strategies. With caseloads <strong>of</strong>ten averaging 150 to 200 <strong>of</strong>fenders per probation <strong>of</strong>ficer in a given agency, supervision levels vary from weekly or twice-weekly meetings for the highest-risk or delinquent probationers to telephone reporting for those towards the end <strong>of</strong> their sentences. Some probationers simply mail in a card to confirm their current address (Petersilia & Turner, 1993). Over the last thirty years, the use <strong>of</strong> probation has grown in response to (and as a result <strong>of</strong>) increasing prison populations. In this climate, intensive supervision probation (ISP) emerged as a supervision strategy that was deemed punitive enough to be used with <strong>of</strong>fenders who would otherwise have been incarcerated, yet cheaper than the cost <strong>of</strong> keeping someone in prison. While few ISP programs are exactly alike, they usually involve a reduction in caseload size and increased frequency <strong>of</strong> contact with the probation <strong>of</strong>ficer, increased drug testing requirements and service provision or brokerage. <strong>The</strong> first wave <strong>of</strong> research in the 1960s was generally described as the “search for the magic number” (Carter & Wilkins, 1976) because it involved experimentation with caseload size to find the optimal ratio <strong>of</strong> <strong>of</strong>fenders to probation <strong>of</strong>ficers. <strong>The</strong> rationale was that smaller caseloads would allow probation <strong>of</strong>ficers to spend more time helping 56
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University of Penn
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In memory of Edwar
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Janel McCaffrey and Knakiya Hagans,
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traditional practice, we find no ev
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Methodology .......................
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LIST OF FIGURES Figure 1.1: Effect
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intensity makes no difference to re
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isk of committing
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impact of probatio
Page 19 and 20: which perhaps suggests that ISP is
Page 21 and 22: program intended to reduce the stra
Page 23 and 24: supervision with probation
Page 25 and 26: 3. To what extent do the risk/need
Page 27 and 28: Methods Scale (SMS: Farrington et a
Page 29 and 30: Types of Outcomes
Page 31 and 32: Description of met
Page 33 and 34: actual behavior. We analyze technic
Page 35 and 36: Treatment of quali
Page 37 and 38: treatment group, and those to the r
Page 39 and 40: Discussion of Resu
Page 41 and 42: were evaluated in the same report (
Page 43 and 44: direction of effec
Page 45 and 46: To what extent does program philoso
Page 47 and 48: Which other components of</
Page 49 and 50: .450; technical violations: Q B < .
Page 51 and 52: it should be noted that those progr
Page 53 and 54: up in unpublished reports than thos
Page 55 and 56: violations first went before a judg
Page 57 and 58: 2 One U.S. estimate indicates that
Page 59 and 60: Tables Table 1.1: Characteristics <
Page 61 and 62: Table 1.3: Moderator Variable <stro
Page 63 and 64: Table 1.4: Moderator Variable <stro
Page 65 and 66: Figures Figure 1.1: Effect
Page 67 and 68: Figure 1.5: Effect of</stro
Page 69: CHAPTER 2. ‘Low-Intensity
Page 73 and 74: to do with it. Furthermore, the mor
Page 75 and 76: to the New York City and Oregon stu
Page 77 and 78: Barnes et al. (forthcoming) have su
Page 79 and 80: paradox.” He suggests that ‘not
Page 81 and 82: fatal shootings in Philadelphia are
Page 83 and 84: the proportions of
Page 85 and 86: Interventions and Follow-Up Probati
Page 87 and 88: officers more <str
Page 89 and 90: 1. Does the effect of</stro
Page 91 and 92: all, resulting in a large number <s
Page 93 and 94: Differential treatment take-up and
Page 95 and 96: control group members who received
Page 97 and 98: effect of LIS on r
Page 99 and 100: incarcerated must also be removed f
Page 101 and 102: offenders in our s
Page 103 and 104: ecidivists in the sample, 77 commit
Page 105 and 106: (IRR = 1.41, p ≤ .107). SES did n
Page 107 and 108: probability of fai
Page 109 and 110: The count model ou
Page 111 and 112: estimate is also practically identi
Page 113 and 114: twice the risk of
Page 115 and 116: an end. On the other hand, research
Page 117 and 118: low-intensity supervision would imp
Page 119 and 120: Conclusion The aim
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We found that low-risk of</
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7 LIS participants who were transfe
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Tables Table 2.1: Sample Characteri
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Table 2.3: Frequency of</st
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Table 2.5: Instrumental Variables M
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Table 2.7: Prevalence of</s
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Table 2.9: Time to Failure (Violent
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Table 2.12: Treatment Effect by Sub
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Table 2.14: Frequency of</s
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Table 2.16: Instrumental Variables
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Figures Figure 2.1: Risk-Based Allo
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Figure 2.5: Survival Time for LIS E
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CHAPTER 3. Risk Prediction for Effe
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sensitivity of the
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Programs should be designed to adhe
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weight information in an appropriat
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statistical model that would predic
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The Philadelphia m
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assignment pool. Backfill probation
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Little research has been done on th
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already predicted to be at low risk
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It assumes that if of</stro
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involved. Crimes that obviously met
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We examine the potential interactio
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age is based on this date regardles
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obberies. The only
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of low-intensity s
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classified as low risk. The
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All the risk ratios in Table 3.9 in
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0.55. Raising the threshold results
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are partly selected on the basis <s
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make up the majority of</st
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strategy allows probation o
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Notes 1 In this study, murder, atte
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Table 3.2: Sample Characteristics (
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Table 3.6: Predictive Ability at Al
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Table 3.9: Post-Random Assignment O
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APPENDICES Appendix A: Systematic R
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combination. The s
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B. ELIGIBILITY CHECKLIST B1. First
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Other: txcxloth C6d. Additional pro
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D. SAMPLE LEVEL CODING SHEET Instru
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E. DEPENDENT VARIABLE CODING SHEET
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F14. Comparison group sample size f
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weight w is then recalculated (6),
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Study Sheffield (Males) 1976 (Folka
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Study Dallas TX 1992 (Turner & Pete
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Study Minnesota ISR 1995 (Deschenes
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Study Clark WA 2003 (Barnoski, 2003
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Study Whatcom WA 2003 (Barnoski, 20
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Study Los Angeles CA 2005 (Zhang &
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Fed. Prob., 56, 12-17. Turner, Pete
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Study Reference Benekos & Sonnenber
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Study Reference Giblin (2002) Using
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Study Reference Lasater et al. (n.d
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Study Reference Robertson (2000) Co
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Study Reference Turner & Greene (19
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Appendix E: Philadelphia APPD Low-<
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Prevalence of Reci
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Frequency of Recid
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Instrumental Variables Model: Preva
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Instrumental Variables Model: Preva
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Post-RA Months in Jail and Probabil
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Scaled Schoenfeld Residuals Time =
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BIBLIOGRAPHY Chapter 1 ** Denotes t
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Cook, T. D., & Campbell, D. T. (197
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Glaze, L. E, & Bonczar, T. P. (2009
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MacKenzie, D. L. (2006a). What work
Page 257 and 258:
Petersilia, J., Turner, S., & Desch
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safety first: 13 strategies for suc
Page 261 and 262:
Wilson, D. B. (2010). Meta-analysis
Page 263 and 264:
Angrist, J. D. (2006). Instrumental
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Clear, T. R., & Hardyman, P. L. (19
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period266.pdf Lowenkamp, C. T., Lat
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Rosch, J. (2006). Deviant peer cont
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Taxman, F. S., & Thanner, M. (2006)
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statistical learning. Journal <stro
Page 275 and 276:
Federal Bureau of
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Marsh, K., & Fox, C., (2008). <stro
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Sherman, L. W. (1993). Defiance, de
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